The Out of Darkness project - What makes an impactful digital storytelling tool?
Bibliographic record
Abstract
Background University students continue to suffer from mental illness at staggering rates. Currently, arts-based approaches have been found to increase mental health literacy in educational settings, such as universities. Specifically, digital storytelling interventions have shown promise in addressing mental health awareness and stigmas related to mental illness. Methods This study involves McMaster University students viewing a set of narrative films from the Out of Darkness project, followed by a semi-structured focus group discussion to determine what elements of the short films made them impactful. Thematic analysis was used to extract these themes. Results The university students participating in the study identified the following three aspects that made the films impactful: connection to the films, the humanization of illness and the emotionality and vulnerability of the individuals depicted in the films. Conclusion The results of this qualitative study suggest that digital storytelling interventions can be effective in addressing stigma surrounding mental health disorders and certain elements of narrative films allow for attitude change within the viewers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".